Optimal bundling of transmembrane helices using sparse distance constraints

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

Characterization of Protein Conformational Changes with Sparse Spin-Label Distance Constraints

The combination of site-directed spin labeling with pulse EPR distance measurements can provide a moderate number of distance constraints on the nanometer length scale for proteins in different states. By adapting an existing algorithm (Zheng, W.; Brooks, B. R. Biophys. J. 2006, 90, 4327) to the problem, we address the question to what extent conformational change can be characterized when the protein structure is known for one of the states, whereas only a sparse set of distance constraints between spin labels is available for the other state. We find that the type and general direction of the conformational change can be recognized, while the amplitude may be uncertain.

Folding elastic transmembrane helices to fit in a low-resolution image by electron microscopy

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

A photon-free approach to transmembrane protein structure determination

The structures of membrane proteins are generally solved using samples dissolved in micelles, bicelles, or occasionally phospholipid bilayers using X-ray diffraction or magnetic resonance. Because these are less than perfect mimics of true biological membranes, the structures are often confirmed by evaluating the effects of mutations on the properties of the protein in their native cellular environments. Low-resolution structures are also sometimes generated from the results of site-directed mutagenesis when other structural data are incomplete or not available. Here, we describe a rapid and automated approach to determine structures from data on site-directed mutants for the special case of homo-oligomeric helical bundles. The method uses as input an experimental profile of the effects of mutations on some property of the protein. This profile is then interpreted by assuming that positions that have large effects on structure/function when mutated project toward the center of the oligomeric bundle. Model bundles are generated, and correlation analysis is used to score which structures have inter-subunit C(β) distances between adjoining monomers that best correlate with the experimental profile. These structures are then clustered and refined using energy-based minimization methods. For a set of 10 homo-oligomeric TM protein structures ranging from dimers to pentamers, we show that our method predicts structures to within 1-2 Å backbone RMSD relative to X-ray and NMR structures. This level of agreement approaches the precision of NMR structures solved in different membrane mimetics.

Amino acid interaction preferences in helical membrane proteins

Membrane proteins are involved in a number of important biological functions. Yet, they are poorly understood from the structure and folding point of view. The external environment being drastically different from that of globular proteins, the intra-protein interactions in membrane proteins are also expected to be different. Hence, statistical potentials representing the features of inter-residue interactions based exclusively on the structures of membrane proteins are much needed. Currently, a reasonable number of structures are available, making it possible to undertake such an analysis on membrane proteins. In this study we have examined the inter-residue interaction propensities of amino acids in the membrane spanning regions of the alpha-helical membrane (HM) proteins. Recently we have shown that valuable information can be obtained on globular proteins by the evaluation of the pair-wise interactions of amino acids by classifying them into different structural environments, based on factors such as the secondary structure or the number of contacts that a residue can make. Here we have explored the possible ways of classifying the intra-protein environment of HM proteins and have developed scoring functions based on different classification schemes. On evaluation of different schemes, we find that the scheme which classifies amino acids to different intra-contact environment is the most promising one. Based on this classification scheme, we also redefine the hydrophobicity scale of amino acids in HM proteins.

Simplified modeling approach suggests structural mechanisms for constitutive activation of the C5a receptor

Molecular modeling of conformational changes occurring in the transmembrane region of the complement factor 5a receptor (C5aR) during receptor activation was performed by comparing two constitutively active mutants (CAMs) of C5aR, NQ (I124N/L127Q), and F251A, to those of the wild-type C5aR and NQ-N296A (I124N/L127Q/N296A), which have the wild-type phenotype. Modeling involved comprehensive sampling of various rotations of TM helices aligned to the crystal template of the dark-adapted rhodopsin along their long axes. By assuming that the relative energies of the spontaneously activated states of CAMs should be lower or at least comparable to energies characteristic for the ground states, we selected the plausible models for the conformational states associated with constitutive activation in C5aR. The modeling revealed that the hydrogen bonds between the side chains of D82-N119, S85-N119, and S131-C221 characteristic for the ground state were replaced by the hydrogen bonds D82-N296, N296-Y300, and S131-R134, respectively, in the activated states. Also, conformational transitions that occurred upon activation were hindered by contacts between the side chains of L127 and F251. The results rationalize the available data of mutagenesis in C5aR and offer the first specific molecular mechanism for the loss of constitutive activity in NQ-N296A. Our results also contributed to understanding the general structural mechanisms of activation in G-protein-coupled receptors lacking the "ionic lock", R(3.50) and E/D(6.30). Importantly, these results were obtained by modeling approaches that deliberately simplify many elements in order to explore potential conformations of GPCRs involving large-scale molecular movements.

Elucidating the higher-order structure of biopolymers by structural probing and mass spectrometry: MS3D

Chemical probing represents a very versatile alternative for studying the structure and dynamics of substrates that are intractable by established high-resolution techniques. The implementation of MS-based strategies for the characterization of probing products has not only extended the range of applicability to virtually all types of biopolymers but has also paved the way for the introduction of new reagents that would not have been viable with traditional analytical platforms. As the availability of probing data is steadily increasing on the wings of the development of dedicated interpretation aids, powerful computational approaches have been explored to enable the effective utilization of such information to generate valid molecular models. This combination of factors has contributed to making the possibility of obtaining actual 3D structures by MS-based technologies (MS3D) a reality. Although approaches for achieving structure determination of unknown targets or assessing the dynamics of known structures may share similar reagents and development trajectories, they clearly involve distinctive experimental strategies, analytical concerns and interpretation paradigms. This Perspective offers a commentary on methods aimed at obtaining distance constraints for the modeling of full-fledged structures while highlighting common elements, salient distinctions and complementary capabilities exhibited by methods used in dynamics studies. We discuss critical factors to be addressed for completing effective structural determinations and expose possible pitfalls of chemical methods. We survey programs developed for facilitating the interpretation of experimental data and discuss possible computational strategies for translating sparse spatial constraints into all-atom models. Examples are provided to illustrate how the concerted application of very diverse probing techniques can lead to the solution of actual biological systems.

Membrane protein fragments reveal both secondary and tertiary structure of membrane proteins

Structural data on membrane proteins, while crucial to understanding cellular function, are scarce due to difficulties in applying to membrane proteins the common techniques of structural biology. Fragments of membrane proteins have been shown to reflect, in many cases, the secondary structure of the parent protein with fidelity and are more amenable to study. This chapter provides many examples of how the study of membrane protein fragments has provided new insight into the structure of the parent membrane protein.

Alpha-helical topology prediction and generation of distance restraints in membrane proteins

The field of protein structure prediction has seen significant advances in recent years. Researchers have followed a multitude of approaches, including methods based on comparative modeling, fold recognition and threading, and first-principles techniques. It is noteworthy that the structure prediction of membrane proteins is comparatively less studied by researchers in the field. A membrane protein is characterized by a protein structure that extends into or through the lipid-lipid bilayer of a cell. The structure is influenced by the combination of the hydrophobic bilayer region, the direct interaction with the bilayer, and the aqueous external environment. Due to the difficulty in obtaining reliable experimental structures, accurate computational prediction of membrane proteins is of paramount importance. An optimization model has been developed to predict the interhelical interactions in alpha-helical membrane proteins. A database of alpha-helical membrane proteins of known structure and limited sequence identity can be constructed to develop interaction probabilities. By then maximizing the occurrence of highly probable pairwise or three-residue interactions, realistic contacts can be predicted by imposing a number of geometrical constraints. The development of these low distance contacts can provide additional distance restraints for first principles-based approaches to the tertiary structure prediction problem. The proposed approach is shown to successfully predict interhelical contacts in several membrane protein systems, including bovine rhodopsin and the recently released human beta2 adrenergic receptor protein structure.

Structural refinement of membrane proteins by restrained molecular dynamics and solvent accessibility data

We present an approach for incorporating solvent accessibility data from electron paramagnetic resonance experiments in the structural refinement of membrane proteins through restrained molecular dynamics simulations. The restraints have been parameterized from oxygen (PiO(2)) and nickel-ethylenediaminediacetic acid (PiNiEdda) collision frequencies, as indicators of lipid or aqueous exposed spin-label sites. These are enforced through interactions between a pseudoatom representation of the covalently attached Nitroxide spin-label and virtual "solvent" particles corresponding to O(2) and NiEdda in the surrounding environment. Interactions were computed using an empirical potential function, where the parameters have been optimized to account for the different accessibilities of the spin-label pseudoatoms to the surrounding environment. This approach, "pseudoatom-driven solvent accessibility refinement", was validated by refolding distorted conformations of the Streptomyces lividans potassium channel (KcsA), corresponding to a range of 2-30 A root mean-square deviations away from the native structure. Molecular dynamics simulations based on up to 58 electron paramagnetic resonance restraints derived from spin-label mutants were able to converge toward the native structure within 1-3 A root mean-square deviations with minimal computational cost. The use of energy-based ranking and structure similarity clustering as selection criteria helped in the convergence and identification of correctly folded structures from a large number of simulations. This approach can be applied to a variety of integral membrane protein systems, regardless of oligomeric state, and should be particularly useful in calculating conformational changes from a known reference crystal structure.

Predicting transmembrane helix pair configurations with knowledge-based distance-dependent pair potentials

As a first step toward a novel de novo structure prediction approach for alpha-helical membrane proteins, we developed coarse-grained knowledge-based potentials to score the mutual configuration of transmembrane (TM) helices. Using a comprehensive database of 71 known membrane protein structures, pairwise potentials depending solely on amino acid types and distances between C(alpha)-atoms were derived. To evaluate the potentials, they were used as an objective function for the rigid docking of 442 TM helix pairs. This is by far the largest test data set reported to date for that purpose. After clustering 500 docking runs for each pair and considering the largest cluster, we found solutions with a root mean squared (RMS) deviation <2 A for about 30% of all helix pairs. Encouragingly, if only clusters that contain at least 20% of all decoys are considered, a success rate >71% (with a RMS deviation <2 A) is obtained. The cluster size thus serves as a measure of significance to identify good docking solutions. In a leave-one-protein-family-out cross-validation study, more than 2/3 of the helix pairs were still predicted with an RMS deviation <2.5 A (if only clusters that contain at least 20% of all decoys are considered). This demonstrates the predictive power of the potentials in general, although it is advisable to further extend the knowledge base to derive more robust potentials in the future. When compared to the scoring function of Fleishman and Ben-Tal, a comparable performance is found by our cross-validated potentials. Finally, well-predicted "anchor helix pairs" can be reliably identified for most of the proteins of the test data set. This is important for an extension of the approach towards TM helix bundles because these anchor pairs will act as "nucleation sites" to which more helices will be added subsequently, which alleviates the sampling problem.

Computational prediction of atomic structures of helical membrane proteins aided by EM maps

Integral membrane proteins pose a major challenge for protein-structure prediction because only approximately 100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane alpha-helices. However, when an intermediate-resolution electron microscopy (EM) map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: 1), generation of an ensemble of alpha-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; 2), fast optimization of side chains and scoring of the resulting conformations; and 3), refinement of the lowest-scoring conformations with internal coordinate mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional, and solvation energy contributions. In addition, our method implements a penalty term through a so-called tethering map, derived from the EM map, which restrains the positions of the alpha-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL.

Long-range distance determinations in biomacromolecules by EPR spectroscopy

Electron paramagnetic resonance (EPR) spectroscopy provides a variety of tools to study structures and structural changes of large biomolecules or complexes thereof. In order to unravel secondary structure elements, domain arrangements or complex formation, continuous wave and pulsed EPR methods capable of measuring the magnetic dipole coupling between two unpaired electrons can be used to obtain long-range distance constraints on the nanometer scale. Such methods yield reliably and precisely distances of up to 80 A, can be applied to biomolecules in aqueous buffer solutions or membranes, and are not size limited. They can be applied either at cryogenic or physiological temperatures and down to amounts of a few nanomoles. Spin centers may be metal ions, metal clusters, cofactor radicals, amino acid radicals, or spin labels. In this review, we discuss the advantages and limitations of the different EPR spectroscopic methods, briefly describe their theoretical background, and summarize important biological applications. The main focus of this article will be on pulsed EPR methods like pulsed electron-electron double resonance (PELDOR) and their applications to spin-labeled biosystems.

Rigid body refinement of protein complexes with long-range distance restraints from pulsed dipolar ESR

The modeling of protein-protein complexes greatly benefits from the incorporation of experimental distance restraints. Pulsed dipolar electron spin resonance spectroscopy is one such powerful technique for obtaining long-range distance restraints in protein complexes. Measurements of the dipolar interaction between two spins placed specifically within a protein complex give information about the spin-spin separation distance. We have developed a convenient method to incorporate such long-range distance information in the modeling of protein-protein complexes that is based on rigid body refinement of the protein components with the software Crystallography and NMR System (CNS). Factors affecting convergence such as number of restraints, error allocation scheme, and number and position of spin labeling sites were investigated with real and simulated data. The use of 4 to 5 different labeling sites on each protein component was found to provide sufficient coverage for producing accuracies limited by the uncertainty in the spin-label conformation within the complex. With an asymmetric scheme of allocating this uncertainty, addition of simulated restraints revealed the importance of longer distances within a limited set of total restraints. We present two case studies: (1) refinement of the complex formed between the histidine kinase CheA and its coupling protein CheW, and (2) refinement of intra-helical separations in the protein a-synuclein bound to micelles.

G-protein coupled receptor structure

Because of their central role in regulation of cellular function, structure/function relationships for G-protein coupled receptors (GPCR) are of vital importance, yet only recently have sufficient data been obtained to begin mapping those relationships. GPCRs regulate a wide range of cellular processes, including the senses of taste, smell, and vision, and control a myriad of intracellular signaling systems in response to external stimuli. Many diseases are linked to GPCRs. A critical need exists for structural information to inform studies on mechanism of receptor action and regulation. X-ray crystal structures of only one GPCR, in an inactive state, have been obtained to date. However considerable structural information for a variety of GPCRs has been obtained using non-crystallographic approaches. This review begins with a review of the very earliest GPCR structural information, mostly derived from rhodopsin. Because of the difficulty in crystallizing GPCRs for X-ray crystallography, the extensive published work utilizing alternative approaches to GPCR structure is reviewed, including determination of three-dimensional structure from sparse constraints. The available X-ray crystallographic analyses on bovine rhodopsin are reviewed as the only available high-resolution structures for any GPCR. Structural information available on ligand binding to several receptors is included. The limited information on excited states of receptors is also reviewed. It is concluded that while considerable basic structural information has been obtained, more data are needed to describe the molecular mechanism of activation of a GPCR.

Assembly of transmembrane helices of simple polytopic membrane proteins from sequence conservation patterns

The transmembrane (TM) domains of most membrane proteins consist of helix bundles. The seemingly simple task of TM helix bundle assembly has turned out to be extremely difficult. This is true even for simple TM helix bundle proteins, i.e., those that have the simple form of compact TM helix bundles. Herein, we present a computational method that is capable of generating native-like structural models for simple TM helix bundle proteins having modest numbers of TM helices based on sequence conservation patterns. Thus, the only requirement for our method is the presence of more than 30 homologous sequences for an accurate extraction of sequence conservation patterns. The prediction method first computes a number of representative well-packed conformations for each pair of contacting TM helices, and then a library of tertiary folds is generated by overlaying overlapping TM helices of the representative conformations. This library is scored using sequence conservation patterns, and a subsequent clustering analysis yields five final models. Assuming that neighboring TM helices in the sequence contact each other (but not that TM helices A and G contact each other), the method produced structural models of Calpha atom root-mean-square deviation (CA RMSD) of 3-5 A from corresponding crystal structures for bacteriorhodopsin, halorhodopsin, sensory rhodopsin II, and rhodopsin. In blind predictions, this type of contact knowledge is not available. Mimicking this, predictions were made for the rotor of the V-type Na(+)-adenosine triphosphatase without such knowledge. The CA RMSD between the best model and its crystal structure is only 3.4 A, and its contact accuracy reaches 55%. Furthermore, the model correctly identifies the binding pocket for sodium ion. These results demonstrate that the method can be readily applied to ab initio structure prediction of simple TM helix bundle proteins having modest numbers of TM helices.

Progress in structure prediction of α-helical membrane proteins

Transmembrane (TM) proteins comprise 20-30% of the genome but, because of experimental difficulties, they represent less than 1% of the Protein Data Bank. The dearth of membrane protein structures makes computational prediction a potentially important means of obtaining novel structures. Recent advances in computational methods have been combined with experimental data to constrain the modeling of three-dimensional structures. Furthermore, threading and ab initio modeling approaches that were effective for soluble proteins have been applied to TM domains. Surprisingly, experimental structures, proteomic analyses and bioinformatics have revealed unexpected architectures that counter long-held views on TM protein structure and stability. Future computational and experimental studies aimed at understanding the thermodynamic and evolutionary bases of these architectural details will greatly enhance predictive capabilities.

Structure modeling of all identified G protein-coupled receptors in the human genome

G protein–coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C_α root-mean-squared deviation from native of 4.6 Å, with a root-mean-squared deviation in the transmembrane helix region of 2.1 Å. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).

G protein–coupled receptors (GPCRs) are a large superfamily of integral membrane proteins that transduce signals across the cell membrane. Because of the breadth and importance of the physiological roles undertaken by the GPCR family, many of its members are important pharmacological targets. Although the knowledge of a protein's native structure can provide important insight into understanding its function and for the design of new drugs, the experimental determination of the three-dimensional structure of GPCR membrane proteins has proved to be very difficult. This is demonstrated by the fact that there is only one solved GPCR structure (from bovine rhodopsin) deposited in the Protein Data Bank library. In contrast, there are no human GPCR structures in the Protein Data Bank. To address the need for the tertiary structures of human GPCRs, using just sequence information, the authors use a newly developed threading-assembly-refinement method to generate models for all 907 registered GPCRs in the human genome. About 820 GPCRs are anticipated to have correct topology and transmembrane helix arrangement. A subset of the resulting models is validated by comparison with mutagenesis experimental data, and consistent agreement is demonstrated.

Solving the membrane protein folding problem

One of the great challenges for molecular biologists is to learn how a protein sequence defines its three-dimensional structure. For many years, the problem was even more difficult for membrane proteins because so little was known about what they looked like. The situation has improved markedly in recent years, and we now know over 90 unique structures. Our enhanced view of the structure universe, combined with an increasingly quantitative understanding of fold determination, engenders optimism that a solution to the folding problem for membrane proteins can be achieved.

A deterministic algorithm for constrained enumeration of transmembrane protein folds

A deterministic algorithm for enumeration of transmembrane protein folds is presented. Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space. The end result is a set of distinct protein conformations, which can be scored and refined as part of a process designed for computational elucidation of transmembrane protein structures.

Data-driven docking for the study of biomolecular complexes

With the amount of genetic information available, a lot of attention has focused on systems biology, in particular biomolecular interactions. Considering the huge number of such interactions, and their often weak and transient nature, conventional experimental methods such as X-ray crystallography and NMR spectroscopy are not sufficient to gain structural insight into these. A wealth of biochemical and/or biophysical data can, however, readily be obtained for biomolecular complexes. Combining these data with docking (the process of modeling the 3D structure of a complex from its known constituents) should provide valuable structural information and complement the classical structural methods. In this review we discuss and illustrate the various sources of data that can be used to map interactions and their combination with docking methods to generate structural models of the complexes. Finally a perspective on the future of this kind of approach is given.